sequential , functional and model subclassing API in Keras _ day 14

In our last blog on day 13, we explained what’s Keras and we showed a code example which was using the Sequential API but did not discuss its API type. Understanding Keras APIs and Their Use Cases In our previous blog post on Day 13, we introduced Keras and provided a code example using the Sequential API. In this post, we will delve into the different types of Keras APIs: Sequential, Functional, and Model Subclassing. We will explain each API, its inventor, appropriate use cases, and whether they can be used interchangeably. We will also analyze the code examples provided to illustrate the differences between these approaches. Sequential API Inventor: François Chollet, the creator of Keras. Overview: The Sequential API is the simplest and most straightforward way to build a neural network in Keras. It allows you to create a model layer-by-layer in a linear stack. Use Cases: – Simple models with a single input and a single output.- Beginners and quick prototyping.- Basic feedforward neural networks and simple CNNs. Mathematical Foundation: The Sequential API models are compositions of functions, where each layer applies a transformation :

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This means the output of one layer is the input to the next. Advantages:- Easy to use and understand.- Ideal for simple, linear architectures. Limitations:- Limited flexibility: cannot handle models with multiple inputs/outputs or complex topologies like shared layers and residual connections. Performance:- Fast to set up and train for simple models, but less efficient for complex architectures due to its limitations in handling non-linear connections and multiple inputs/outputs. For more information, visit the [official Keras documentation](https://keras.io/api/models/sequential/). Functional API Inventor: François Chollet. Overview: The Functional API is a more flexible way to build models. It allows for the creation of complex models with multiple inputs and outputs, shared layers, and non-linear connections. Use Cases: Models with multiple inputs and outputs. Complex architectures like branching and merging paths. Shared layers, such as in Siamese networks. Mathematical Foundation: The Functional API models are represented as Directed Acyclic Graphs (DAGs) of layers:

   

This flexibility allows for constructing more complex architectures. Advantages: Supports arbitrary model architectures. Suitable for advanced architectures such as residual networks and multi-modal inputs. Limitations: Slightly more complex to understand and use compared to the Sequential API. Performance: Efficient for complex architectures due to its ability to handle multiple inputs/outputs and non-linear connections. For more details, visit the Keras documentation. Model Subclassing API Overview: The Model Subclassing API provides the highest level of flexibility and control. It involves creating a custom model by subclassing the tf.keras.Model class and defining the layers and forward pass manually. Use Cases: Research and development of novel architectures. Models requiring custom behaviors and complex operations. Mathematical Foundation: In this API, you explicitly define the forward pass in the call method, giving full control over data flow through the layers:

   

This method allows for implementing complex operations and unique behaviors. Advantages: Maximum flexibility and control. Ideal for custom behaviors and complex models. Limitations: Requires a deeper understanding of Keras and TensorFlow. More complex to implement compared to the Sequential and Functional APIs. Performance: Optimal for custom and complex models due to the direct control over the model architecture. For further information, refer to the Keras guide on making new layers and models via subclassing. Comparing the APIs Sequential API: Best for simple, linear models. Functional API: Suitable for complex models with multiple inputs/outputs and non-linear connections. Model Subclassing API: Provides full control and customization, ideal for research and highly specialized models. Can All APIs Be Used for the Same Problem? Flexibility and Choice: While all three APIs can technically be used to solve the same problem, the choice depends on the complexity and requirements of the model. Sequential API: Limited to simple, linear models. Not suitable for complex architectures. Functional API: Offers more flexibility and is suitable for complex models. Preferred for most use cases where complexity is involved. Model Subclassing API: Provides full control and is best for novel or highly customized models. Example: For a simple classification task, the Sequential API is sufficient. For a model with multiple inputs and outputs, the Functional API is better suited. If the model requires custom training loops or complex behaviors, the Model Subclassing API would be the best choice. Other APIs in Keras Keras also includes specialized APIs for preprocessing, tuning, and serialization, among other tasks. These APIs support a wide range of workflows, making Keras a versatile library for deep learning. By understanding the strengths and appropriate use cases of each Keras API, you can select the most suitable approach for your machine learning projects and build models effectively and efficiently. For further reading and detailed information, you can explore the Keras Models API documentation. Now is Time to Code: Detailed Explanations Why Use the Model Subclassing API? Flexibility: Provides the ability to define any architecture and custom behavior, which is especially useful for complex or non-standard models. Customization: Allows for implementing custom layers and operations within the forward pass, offering unparalleled control over the model’s inner workings. Research and Innovation: Ideal for experimenting with novel architectures and approaches in deep learning research. How It Affects the Results: Precision and Control: By having full control over the model’s operations, you can optimize and customize the architecture to potentially achieve better performance and efficiency for specific tasks. Complexity Management: This approach can manage more complex dependencies and layer interactions, which might not be possible with simpler APIs. Mathematical Insight: In the Model Subclassing API, you explicitly define the transformation of the input tensor x through various layers, leading to the output tensor y. This direct control allows for implementing sophisticated transformations and handling multiple inputs and outputs more effectively. By understanding and leveraging these three different APIs in Keras, you can choose the best approach based on your specific needs, complexity, and the nature of your machine learning or deeplearning tasks. Differences in Model Definitions and Explanations The exact lines in each of the three code examples that differ are the lines where the model is defined. These lines are different because each API provides a distinct way to define the architecture of the neural network. Sequential API The model is defined using a sequential approach, where each layer is added one after another in a linear stack. # Sequential API model definition import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=[28, 28]), tf.keras.layers.Dense(300, activation=”relu”), tf.keras.layers.Dense(100, activation=”relu”), tf.keras.layers.Dense(10, activation=”softmax”) ]) Functional API The model is defined using a functional approach, which allows for more complex architectures with multiple inputs and outputs. # Functional API model definition import tensorflow as tf inputs = tf.keras.Input(shape=(28, 28)) x = tf.keras.layers.Flatten()(inputs) x = tf.keras.layers.Dense(300, activation=”relu”)(x) x = tf.keras.layers.Dense(100, activation=”relu”)(x) outputs = tf.keras.layers.Dense(10, activation=”softmax”)(x) model = tf.keras.Model(inputs, outputs) Model Subclassing API The model is defined by subclassing the tf.keras.Model class, providing the most flexibility and control over the model’s architecture and forward pass. # Model Subclassing API model definition import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super(MyModel, self).__init__() self.flatten = tf.keras.layers.Flatten() self.dense1 = tf.keras.layers.Dense(300, activation=”relu”) self.dense2 = tf.keras.layers.Dense(100, activation=”relu”) self.dense3 = tf.keras.layers.Dense(10, activation=”softmax”) def call(self, inputs): x = self.flatten(inputs) x = self.dense1(x) x = self.dense2(x) return self.dense3(x) model = MyModel() These differences exist because: Sequential API: Best for simple models where the layers are stacked in a linear order. Functional API: Suitable for complex models with non-linear topology, multiple inputs/outputs, and shared layers. Model Subclassing API: Provides full control and customization over the model, ideal for research and novel architectures. Comparison of Keras APIs: Sequential, Functional, and Model Subclassing in our code above, is the resuls the same ? Results Comparison Across All 3 APIs The results for models built using the Sequential API, Functional API, and Model Subclassing API will generally be the same, assuming the architectures, initialization, training process, and hyperparameters are identical. The underlying mathematical operations and computations are the same across these APIs; they merely offer different ways to define and build the model. Key Factors for Same Results Identical Architecture: All three APIs use the same layers in the same order: Flatten -> Dense (300 units, ReLU) -> Dense (100 units, ReLU) -> Dense (10 units, Softmax). Same Initialization and Training Process: The random seed is set for reproducibility. The models are compiled with the same loss function (sparse_categorical_crossentropy), optimizer (sgd), and metric (accuracy). The training process (epochs, batch size, validation split) is identical. Given these factors, the results in terms of accuracy and loss should be the same across the three APIs because the underlying computations performed by the models are identical. Still Sequential api is better approach for this task, Now Why Sequential API is Better for This Task? Simplicity and Readability The Sequential API allows for the simplest and most readable code when building straightforward, linear models. This reduces the potential for errors and makes the code easier to understand and maintain. Ease of Use For tasks that do not require complex architectures, the Sequential API is the quickest and easiest way to build a model. There’s no need to define the input and output layers explicitly as in the Functional API or to implement a custom class as in the Model Subclassing API. Focused Scope The Sequential API is designed specifically for models where layers are stacked sequentially. This focused scope makes it a better fit for simple models, avoiding the overhead and additional code required by the other APIs. Results Comparison for the Provided Example The models built using the Sequential API, Functional API, and Model Subclassing API perform the same computations. Therefore, the training and evaluation results (accuracy and loss) for all three models should be the same. Expected Results Assuming the same architecture, initialization, and training procedure, the models should produce identical results. For example: Test accuracy: 0.8745 (expected…